1. Field of the Invention
The present invention relates generally to data compression.
2. Description of the Related Art
The prior art Shannon sampling theorem dictates that any signal needs to be sampled at the Nyquist rate which is the twice the maximum cut off frequency at which the bandwidth of the signal tends towards zero. The Shannon sampling theorem is theoretically valid only for a stationary signal whose second order statistics do not vary with time. In real life, however, most signals are non-stationary, and the spectral content, the center frequency, and the bandwidth of the local signal vary with time. Researchers have attempted several prior art smart sampling approaches to sub sample the signal below the Nyquist rate exploiting the property of time varying spectral characteristics of the signal. Although these attempts have resulted in some improvement in reduced sampling for a class of signals, they did not arrive at lossless compression schemes or even near lossless compression with significant reduction in sampling rates.
In parallel to and complementing the effort of prior art sampling techniques, prior art data compression methods have been developed for lossless compression schemes to reduce the bit rate per sample by using Arithmetic Coding, Run Length Coding, Huffman Compression or Lev-Zimpel-Welch (LZW) and Discrete Cosine Transform (DCT) or Discrete Wavelet Transform (DWT) approaches. The DCT or DWT had also been successfully used for lossy compression with significant bit rates per sample in the JPEG and JPEG 2000 and MPEG standards respectively.